Convergence analysis of distributed stochastic gradient descent with shuffling
نویسندگان
چکیده
منابع مشابه
Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling
When using stochastic gradient descent (SGD) to solve large-scale machine learning problems, a common practice of data processing is to shuffle the training data, partition the data across multiple threads/machines if needed, and then perform several epochs of training on the re-shuffled (either locally or globally) data. The above procedure makes the instances used to compute the gradients no ...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2019
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.01.037